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ease
features
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support

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Description

For many years, the Asset Based Lending (ABL) sector has voiced several concerns. Clients have been seeking a method to archive the AssetReader data, which would allow them to analyze simple trends, compare Borrowing Bases, and monitor concentration balances over time while enabling more precise confirmations through an optional module. The programmers expressed a desire to implement artificial intelligence (AI) to work with the data, while both developers and owners were eager to create innovative solutions that leverage this technology. With an impressive 33 years of combined experience in ABL, encompassing audit, operations, fraud investigations, and esteemed ABL training programs, the FinSoft team has developed a much-needed AI tool that the ABL industry has long awaited—providing easily accessible alerts regarding exceptions, anomalies, and unusual items. This innovative approach aims to enhance decision-making processes and ultimately improve overall efficiency within the sector.

Description

Benford's law serves as a tool for uncovering patterns indicative of improper disbursements. It involves examining audit trail reports from QuickBooks or other bookkeeping software to pinpoint unusual activities like voids and deletions. Additionally, it entails identifying multiple payments made for identical amounts on the same day. A thorough review of payroll runs is conducted to detect any payments exceeding the established salary or hourly rates. Payments made on non-business days are also scrutinized. Statistical calculations help in identifying outliers that may suggest fraudulent activity, and duplicate payments are tested for validation. Vendor files in accounts payable are analyzed for names that may be suspiciously similar, and investigations are conducted to uncover fictitious vendors. Comparisons of vendor and payroll addresses are evaluated using Z-Scores and relative size factor tests. While data monitoring and surprise audits have shown to significantly reduce fraud losses, only 37% of organizations implement these critical controls. For businesses employing fewer than 100 individuals, the average loss due to fraud is estimated at $200,000, highlighting that smaller enterprises often lack the necessary resources to effectively detect and address fraudulent activities. Consequently, it is essential for small businesses to adopt more robust fraud detection mechanisms to safeguard their financial integrity.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

$1,400 one-time payment
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

FinSoft

Website

www.finsoft.net

Vendor Details

Company Name

MedCXO

Country

United States

Website

medcxo.com/fraud/

Product Features

Fraud Detection

Access Security Management
Check Fraud Monitoring
Custom Fraud Parameters
For Banking
For Crypto
For Insurance Industry
For eCommerce
Internal Fraud Monitoring
Investigator Notes
Pattern Recognition
Transaction Approval

Product Features

Fraud Detection

Access Security Management
Check Fraud Monitoring
Custom Fraud Parameters
For Banking
For Crypto
For Insurance Industry
For eCommerce
Internal Fraud Monitoring
Investigator Notes
Pattern Recognition
Transaction Approval

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